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HaiderK
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Assignment

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Transformers
PyTorch
vision-encoder-decoder
Generated from Trainer
Model card Files
xet
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How to use from
vLLM
Install from pip and serve model 
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HaiderK/Assignment"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HaiderK/Assignment",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker 
docker model run hf.co/HaiderK/Assignment
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Assignment

This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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